DP-CL(Continual Learning with Differential Privacy)

Related tags

Deep Learning DP-CL
Overview

DP-CL(Continual Learning with Differential Privacy)

This is the official implementation of the Continual Learning with Differential Privacy.

If you use this code or our results in your research, please cite as appropriate:

@article{desai2021continual,
  title={Continual Learning with Differential Privacy},
  author={Pradnya, Desai and Lai, Phung and Phan, NhatHai and Thai, My},
  journal={International Conference on Neural Information Processing},
  year={2021}
}

Software Requirements

Python 3.7 is used for the current codebase.

Tensorflow 2.5

Experiments

The repository comes with instructions to reproduce the results in the paper or to train the model from scratch:

To reproduce the results:

  • Clone or download the folder from this repository.

  • Please find dataset on Google Drive folder.

  • Go to folder DP-CL/ and Run ./replicate_results_xx.sh xx 3 where xx is the name of dataset and task that you'd like to run. For example: ./replicate_results_mnist.sh MNIST 3 for MNIST, ./replicate_results_cifar100.sh CIFAR 3 for CIFAR-100, ./replicate_results_cifar10.sh CIFAR 3 for CIFAR-10.

Potential issues

If you have any issues while running the code or further information, please send email directly to the first authors of this paper ([email protected] or [email protected]).

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Comments
  • Incorrect noise scale

    Incorrect noise scale

    Hi, something is wrong with the noise scale. It is the code of adding noise to scale. model.py line 1349

    computed_gradient1 += tf.random_normal(shape=tf.shape(computed_gradient1), mean=0.0, stddev = (sigma * sensitivity)**2, dtype=tf.float32)

    The stddev is the standard deviation. At DP-SGD, standard deviation should be sigma * sensitivity rather than with "**2". So it should be

    computed_gradient1 += tf.random_normal(shape=tf.shape(computed_gradient1), mean=0.0, stddev = (sigma * sensitivity), dtype=tf.float32)
    opened by Shua-Kang 1
Owner
Phung Lai
PhD student in Information Systems & Master in Computer Science. Research interests: Differential Privacy, NLP, Interpretable ML.
Phung Lai
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